''' This file provides a wrapper class for AT_HE (https://github.com/ShawnXYang/AT_HE) model for CIFAR-10 dataset. '''

import sys
import os
import math

import torch
import torch.nn as nn
import torch.nn.functional as F
import tensorflow as tf

from ares.model.pytorch_wrapper import pytorch_classifier_with_logits
from ares.utils import get_res_path, download_res

MODEL_PATH = get_res_path('./cifar10/model-wideres-pgdHE-wide10.pt')


def load(_):
    model = AT_HE()
    model.load()
    return model


def download(model_path):
    if not os.path.exists(model_path):
        if not os.path.exists(os.path.dirname(model_path)):
            os.makedirs(os.path.dirname(model_path), exist_ok=True)
        url = 'http://ml.cs.tsinghua.edu.cn/~xiaoyang/downloads/weights/model-wideres-pgdHE-wide10.pt'
        download_res(url, model_path)


@pytorch_classifier_with_logits(n_class=10, x_min=0.0, x_max=1.0,
                                x_shape=(32, 32, 3), x_dtype=tf.float32, y_dtype=tf.int32)
class AT_HE(torch.nn.Module):
    def __init__(self):
        torch.nn.Module.__init__(self)
        self.model = WideResNet(use_FNandWN = True)
        self.model = torch.nn.DataParallel(self.model).cuda()

    def forward(self, x):
        x = x.transpose(1, 2).transpose(1, 3).contiguous()
        labels = self.model((x.cuda()))
        return labels.cpu()

    def load(self):
        checkpoint = torch.load(MODEL_PATH)
        self.model.load_state_dict(checkpoint['state_dict'])
        self.model.eval()


class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                                                                padding=0, bias=False) or None

    def forward(self, x):
        if not self.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class WideResNet(nn.Module):
    def __init__(self, depth=34, num_classes=10, widen_factor=10, dropRate=0.0, use_FNandWN=False, i_normalize=True):
        super(WideResNet, self).__init__()
        nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert ((depth - 4) % 6 == 0)
        n = (depth - 4) / 6
        block = BasicBlock
        self.use_FNandWN = use_FNandWN
        self.i_normalize = i_normalize
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 1st sub-block
        self.sub_block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.ReLU(inplace=True)
        if self.use_FNandWN:
            self.fc = nn.Linear(nChannels[3], num_classes, bias = False)
        else:
            self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear) and not self.use_FNandWN:
                m.bias.data.zero_()
        
    def forward(self, x):
        if self.i_normalize:
            self.mu = torch.tensor((0.4914, 0.4822, 0.4465)).view(3,1,1).cuda()
            self.std = torch.tensor((0.2471, 0.2435, 0.2616)).view(3,1,1).cuda()
            x = (x - self.mu)/self.std
        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        out = self.block3(out)
        out = self.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(-1, self.nChannels)
        if self.use_FNandWN:
            out = F.normalize(out, p=2, dim=1)
            for _, module in self.fc.named_modules():
                if isinstance(module, nn.Linear):
                    module.weight.data = F.normalize(module.weight, p=2, dim=1)
        return self.fc(out)


if __name__ == '__main__':
    download(MODEL_PATH)
